基于深度学习的汽车故障知识图谱构建

doi:10.19562/j.chinasae.qcgc.2023.01.006

Abstract

Abstract:

This paper introduces knowledge graph into the field of automobile fault diagnosis. Taking the after-sales business data of a company as the source， a knowledge graph construction process is designed according to the characteristics of the text， which adds text pre-classification and entity reorganization process based on the traditional construction process. Text pre-classification based on DPCNN model is used to deal with the problem of information redundancy in target fields. The combination of entity extraction based on BERT-BiLSTM-MUL-CRF model and entity reorganization based on grammar rules can effectively solve the problems of nested entities and discontinuous entity problems in text. The method combining term similarity and structural similarity is used to complete knowledge fusion. Finally， Neo4j graph database is used for storage so as to complete the construction of vehicle fault knowledge graph.

Key words: knowledge graph, deep learning, entity extraction

Jie Hu,Yuanjie Li,Hao Geng,Huangzheng Geng,Xiong Guo,Hongwei Yi. Construction of Vehicle Fault Knowledge Graph Based on Deep Learning[J].Automotive Engineering, 2023, 45(1): 52-60.

Figures/Tables 17

References 25

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数据类型	数据字段名称	是否为结构化数据
车辆信息	车型平台	是
	车系	是
	VIN	是
	购车日期	是
	行驶里程	是
故障信息	故障描述	否
故障信息	处理结果	否
维修信息	修理日期	是
	工位名称	是
	配件名称	是

文本类别	示例
故障描述	用户抱怨车辆尾门无法打开
排查流程	测量相关电路正常
故障原因	经检查为尾门拉手内部线路短路所致
更换配件	更换尾门拉手组件

Algorithm 1	基于语法规则的实体匹配
Input：	故障部位实体、失效形式实体在原文本中的索引列表L₁；失效形式实体索引列表L₂
Output：	故障现象实体索引列表L₃
1：	L₃，stack//初始化列表L₃和栈stack
2：	for i ∈L₁ do
3：	if i ∈L₂ then//判断是否为失效形式实体
4：	if stack== null then
5：	stack?i //将失效形式实体索引i存入故障现象实体索引列表L₃
6：	else
7：	for j ∈ stack do
8：	stack.pop（j）//将栈顶元素j弹出
9：	L₃?［i，j］//将故障部位与失效形式组合，存放到L₃中
10：	end for
11：	end if
12：	else
13：	stack?i //将元素i压入栈中
14：	end if
15：	end for
16：	return L₃//输出结果

模型参数	模型参数取值
嵌入层	词向量
卷积核尺寸	3
Dropout	0.3
学习率	2×10^-4
优化器	Adam

文本分类模型	P	R	F₁
SVM	89.33	89.38	89.24
Naive Bayes	85.86	83.97	84.69
TextCNN^［14］	90.91	90.98	90.73
VDCNN^［22］	86.53	84.57	85.24
DPCNN^［13］	93.14	92.79	92.91

Construction of Vehicle Fault Knowledge Graph Based on Deep Learning

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Figures/Tables 17

References 25

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模型参数	模型参数取值
嵌入层	字向量
dropout	0.5
epoch	40
学习率	3×10^-5
优化器	AdamW
注意力头数	8

实体抽取模型	P	R	F₁
IDCNN-CRF^［23］	83.50	83.38	83.44
Lattice LSTM-CRF^［24］	88.75	84.96	86.78
LRCNN-CRF^［25］	88.85	86.92	87.86
BiLSTM-CRF	77.18	79.84	78.48
BERT-BiLSTM-MUL-CRF	91.01	89.38	90.16

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